营销归因与渠道效果分析

原名：attribution

解释不同渠道和报表为何出现差异，用统一口径分析转化来源与成本。

中文 Skills 技能说明

适合老板判断咨询、报名或订单究竟来自哪个渠道。提供脱敏的投放、访问和交易记录，先对齐时间窗口与转化定义，再比较首触、末触或多触点口径，区分相关关系和增量证据。输出归因说明、数据缺口与后续验证方案，不把平台自报结果直接当成真实贡献。可用本地 CSV 做初步分析；接入广告或分析服务要自有账号和权限，身份关联必须满足合法、必要及最小化要求。

上游能力依据

上游原始适用说明：When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo.

上游 SKILL.md 主要章节（保留原文标题）：

Boundaries — what this skill does NOT own
Pillar A — Interpretation
1. What attribution can and can't tell you
2. Attribution models
3. The three measurement paradigms
4. Self-reported attribution
5. Reconciling conflicting sources
6. The blind spots
7. Business-type fork
Pillar B — Own your attribution (first-party)

使用边界

先确认数据口径、只读或写入权限以及备份方案。查询结果需要说明范围，任何结构或数据变更都应先预览并人工确认。

作者、翻译与许可证

原作者: Corey Haines 与 marketingskills contributors
中文翻译: CEOFans翻译
许可证: MIT
上游来源: https://github.com/coreyhaines31/marketingskills/tree/5e721d73ac85be8ba917d6a9ca9cb5bc98f02b80/skills/attribution

适用范围

平台：linux、macos、windows；标签：数据与数据库、营销归因与渠道效果分析、开源技能

安全提示

基础静态扫描不等于绝对安全。技能可能调用命令、浏览器、云服务或本地文件，请在最小权限环境中使用，高风险操作必须人工确认。

如发现侵权、许可证或安全问题，可在本页前台提交投诉，管理员复核后可立即下架。